wehub-resource-sync
66 lines
3.3 KiB
Markdown
66 lines
3.3 KiB
Markdown
# Node classification on heterogeneous graph with RGCN
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This example aims to demonstrate how to run node classification task on heterogeneous graph with **DGL**. Models are not tuned to achieve the best accuracy yet.
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## Run on `ogbn-mag` dataset
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In the preprocess stage, reverse edges are added and duplicate edges are removed. Feature data of `author` and `institution` node types are generated dynamically with embedding layer.
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### Sample on CPU and train/infer on CPU
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```
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python3 hetero_rgcn.py --dataset ogbn-mag
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```
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### Sample on CPU and train/infer on GPU
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```
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python3 hetero_rgcn.py --dataset ogbn-mag --num_gpus 1
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```
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### Resource usage and time cost
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Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 96 vCPUs(Cascade Lake P-8259L), 8 NVIDIA T4 GPUs(16GB RAM). CPU RAM usage is the peak value of `used` field of `free` command which is a bit rough. Please refer to `RSS`/`USS`/`PSS` which are more accurate. GPU RAM usage is the peak value recorded by `nvidia-smi` command.
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| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
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| ------------ | ------------- | ----------- | ------------- | ------------------------ |
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| ~1.1GB | ~7GB | 0 | 0GB | ~233s |
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| ~1.1GB | ~5GB | 1 | 4.5GB | ~73.6s |
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### Accuracies
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```
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Epoch: 01, Loss: 2.3386, Valid: 47.67%, Test: 46.96%
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Epoch: 02, Loss: 1.5563, Valid: 47.66%, Test: 47.02%
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Epoch: 03, Loss: 1.1557, Valid: 46.58%, Test: 45.42%
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Test accuracy 45.3850
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```
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## Run on `ogb-lsc-mag240m` dataset
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In the preprocess stage, reverse edges are added and duplicate edges are removed. What's more, feature data are generated in advance for `author` and `institution` node types via message passing. Since such preprocessing will usually take a long time, we also offer the above files for download:
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* [`paper-feat.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/paper-feat.npy)
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* [`author-feat.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/author-feat.npy)
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* [`inst-feat.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/inst-feat.npy)
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* [`hetero-graph.dgl`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/hetero-graph.dgl)
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### Sample on CPU and train/infer on CPU
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```
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python3 hetero_rgcn.py --dataset ogb-lsc-mag240m
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```
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### Sample on CPU and train/infer on GPU
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```
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python3 hetero_rgcn.py --dataset ogb-lsc-mag240m --num_gpus 1
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```
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### Resource usage and time cost
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Below results are roughly collected from an AWS EC2 **g4dn.metal**, 384GB RAM, 96 vCPUs(Cascade Lake P-8259L), 8 NVIDIA T4 GPUs(16GB RAM). CPU RAM usage is the peak value of `used` field of `free` command which is a bit rough. Please refer to `RSS`/`USS`/`PSS` which are more accurate. GPU RAM usage is the peak value recorded by `nvidia-smi` command.
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| Dataset Size | CPU RAM Usage | Num of GPUs | GPU RAM Usage | Time Per Epoch(Training) |
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| ------------ | ------------- | ----------- | ------------- | ------------------------ |
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| ~404GB | ~72GB | 0 | 0GB | ~325s |
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| ~404GB | ~61GB | 1 | 14GB | ~178s |
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### Accuracies
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```
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Epoch: 01, Loss: 2.0798, Valid: 52.04%
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Epoch: 02, Loss: 1.8652, Valid: 54.51%
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Epoch: 03, Loss: 1.8175, Valid: 53.71%
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```
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